As organizations move from prototyping LLMs to deploying them in production, the biggest challenges are no longer about model accuracy - they’re about trust, security, and control. How do we monitor model behavior, prevent prompt injection, track drift, and enforce governance across environments?

This talk presents a real-world view of how to design secure and governed LLM pipelines, grounded in open-source tooling and reproducible architectures. We’ll discuss how multi-environment setups (sandbox, runner, production) can isolate experimentation from deployment, how to detect drift and hallucination using observability metrics, and how to safeguard against prompt injection, data leakage, and bias propagation.

Attendees will gain insight into how tools like MLflow, Ray, and TensorFlow Data Validation can be combined for ** version tracking, monitoring, and auditability**, without turning your workflow into a black box. By the end of the session, you’ll walk away with a practical roadmap on what makes an LLMOps stack resilient: reproducibility by design, continuous evaluation, and responsible governance across the LLM lifecycle.

As AI continues to shape human-computer interaction, there’s a growing opportunity and responsibility to ensure these technologies serve everyone, including people with communication disabilities. In this talk, I will present my ongoing work in developing a real-time American Sign Language (ASL) recognition system, and explore how integrating accessible design principles into AI research can expand both usability and impact.

The core of the talk will cover the Sign Language Recogniser project (available on GitHub), in which I used MediaPipe Studio together with TensorFlow, Keras, and OpenCV to train a model that classifies ASL letters from hand-tracking features.

I’ll share the methodology: data collection, feature extraction via MediaPipe, model training, and demo/testing results. I’ll also discuss challenges encountered, such as dealing with gesture variability, lighting and camera differences, latency constraints, and model generalization.

Beyond the technical implementation, I’ll reflect on the broader implications: how accessibility-focused AI projects can promote inclusion, how design decisions affect trust and usability, and how women in AI & data science can lead innovation that is both rigorous and socially meaningful. Attendees will leave with actionable insights for building inclusive AI systems, especially in domains involving rich human modalities such as gesture or sign.

If we want to run data science workloads (e.g. using Tensorflow, PyTorch, and others) in containers (for local development or production on Kubernetes), we need to build container images. Doing that with a Dockerfile is fairly straightforward, but is it the best method? In this talk, we'll take a well-known speech-to-text model (Whisper) and show various ways to run it in containers, comparing the outcomes in terms of image size and build time.

The US Army Corps of Engineers (USACE) is responsible for maintaining and improving nearly 12,000 miles of shallow-draft (9'-14') inland and intracoastal waterways, 13,000 miles of deep-draft (14' and greater) coastal channels, and 400 ports, harbors, and turning basins throughout the United States. Because these components of the national waterway network are considered assets to both US commerce and national security, they must be carefully managed to keep marine traffic operating safely and efficiently.

The National DQM Program is tasked with providing USACE a nationally standardized remote monitoring and documentation system across multiple vessel types with timely data access, reporting, dredge certifications, data quality control, and data management. Government systems have often lagged commercial systems in modernization efforts, and the emergence of the cloud and Data Lakehouse Architectures have empowered USACE to successfully move into the modern data era.

This session incorporates aspects of these topics: Data Lakehouse Architecture: Delta Lake, platform security and privacy, serverless, administration, data warehouse, Data Lake, Apache Iceberg, Data Mesh GIS: H3, MOSAIC, spatial analysis data engineering: data pipelines, orchestration, CDC, medallion architecture, Databricks Workflows, data munging, ETL/ELT, lakehouses, data lakes, Parquet, Data Mesh, Apache Spark™ internals. Data Streaming: Apache Spark Structured Streaming, real-time ingestion, real-time ETL, real-time ML, real-time analytics, and real-time applications, Delta Live Tables. ML: PyTorch, TensorFlow, Keras, scikit-learn, Python and R ecosystems data governance: security, compliance, RMF, NIST data sharing: sharing and collaboration, delta sharing, data cleanliness, APIs.

Talk by: Jeff Mroz

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JavaScript remains the top programming language today with most code repositories written using JavaScript on GitHub. However, JavaScript is evolving beyond just a language for web application development into a language built for tomorrow. Everyday tasks like data wrangling, data analysis, and predictive analytics are possible today directly from a web browser. For example, many popular data analytics libraries, like Tensorflow.js, now support JavaScript SDKs.

Another popular library, Danfo.js, makes it possible to wrangle data using familiar pandas-like operations, shortening the learning curve and arming the typical data engineer or data scientist with another data tool in their toolbox. In this presentation, we’ll explore using the Node.js connector for Delta Sharing to build a data analytics app that summarizes a Twitter dataset.

Talk by: Will Girten

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ABOUT THE TALK: Quarto is a multi-language, open-source toolkit for creating data-driven websites, reports, presentations, and scientific articles, built on Jupyter.

This talk teaches you how to use Quarto to publish Jupyter notebooks as production quality websites, books, blogs, presentations, PDFs, Office documents, and more. It covers how to publish notebooks within existing content management systems like Hugo, Docusaurus, and Confluence and also explore how Quarto works under the hood along with how the system can be extended to accommodate unique requirements and workflows.

ABOUT THE SPEAKER: J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown publishing ecosystem and has also worked extensively on the R interfaces to Python and TensorFlow. J.J. is now leading the Quarto project, which is a new Jupyter-based scientific and technical publishing system.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

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In our presentation, we will walk through a model created to predict repeat admissions to substance abuse treatment centers. The goal is to predict early who will be at high risk for relapse so care can be tailored to put additional focus on these patients. We used the Treatment Episode Data Set (TEDS) Admissions data set, which includes every publicly funded substance abuse treatment admission in the US.

While longitudinal data is not available in the data set, we were able to predict with 88% accuracy and an f-score of 0.85 which admissions were first or repeat admissions. Our solution used a scikit-learn Random Forest model and leveraged MLFlow to track model metrics to choose the most effective model. Our pipeline tested over 100 models of different types ranging from Gradient Boosted Trees to Deep Neural Networks in Tensorflow.

To improve model interpretability, we used Shapley values to measure which variables were most important for predicting readmission. These model metrics along with other valuable data are visualized in an interactive Power BI dashboard designed to help practitioners understand who to focus on during treatment. We are in discussions with companies and researchers who may be able to leverage this model in substance abuse treatment centers in the field.

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Using Tensorflow with big datasets has been an impediment for building deep learning models due to the added complexities of running it in a distributed setting and complicated MLOps code, recent advancements in tensorflow 2, and some extension libraries for Spark has now simplified a lot of this. This talk focuses on how we can leverage the best of both Spark and tensorflow to build machine learning and deep learning models using minimal MLOps code letting Spark handle the grunt of work, enabling us to focus more on feature engineering and building the model itself. This design also enables us to use any of the libraries in the tensorflow ecosystem (like tensorflow recommenders) with the same boilerplate code. For businesses like ours, fast prototyping and quick experimentations are key to building completely new experiences in an efficient and iterative way. It is always preferable to have tangible results before putting more resources into a certain project. This design provides us with that capability and lets us spend more time on research, building models, testing quickly, and rapidly iterating. It also provides us with the flexibility to use our choice of framework at any stage of the machine learning lifecycle. In this talk, we will go through some of the best and new features of both spark and tensorflow, how to go from single node training to distributed training with very few extra lines of code, how to leverage MLFlow as a central model store, and finally, using these models for batch and real-time inference.

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